Receptor-based Methods Research Articles

A new application of multi-criteria decision making in identifying critical dust sources and comparing three common receptor-based models

Dust storms are a common phenomenon in arid and semi-arid regions in West Asia, which has led to high levels of PM10 in local and remote area. The Yazd city in Iran with a high PM10 level located downstream of dust sources in the Middle East and Central Asia. In this study, based on meteorological and PM10 monitoring data, backward trajectory modeling of air parcels related to dust events at Yazd station was performed using the HYSPLIT model in 2012–2019. The trajectory cluster analysis was used to identify the main dust transport pathways and wind systems. Three methods of Cross-referencing Backward Trajectory (CBT), Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) were used to identify the most critical dust sources. Multi-Criteria Decision Making (MCDM) methods were also used to integrate the results. Nine dust sources affecting central Iran were determined, and six criteria from different aspects were considered. To prioritize the dust sources affecting central Iran from four new MCDM methods, including WASPAS, EDAS, ARAS and TOPSIS were used. The results showed that the Levar wind system (51%), the Shamal wind system (32%) and the Prefrontal wind system (18%) were the most important wind systems to cause dust events in central Iran. The MCDM approach to identify dust sources also showed that Dasht-e-Kavir in central Iran was the most critical dust source. The results also showed that in hot seasons (spring and summer), local and Central Asia dust sources and cold seasons (autumn and winter), Middle East dust sources have the greatest impact on dust events in central Iran. Also, a comparison of common receptor-based methods for identifying dust sources showed that CBT, CWT and PSCF were the most appropriate methods for identifying dust sources, respectively.

Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds

Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein-ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein-ligand interactions are known. Receptor-based methods predicting protein-ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein-ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.

Advantages and Limitations of Androgen Receptor-Based Methods for Detecting Anabolic Androgenic Steroid Abuse as Performance Enhancing Drugs.

Testosterone (T) and related androgens are performance enhancing drugs (PEDs) abused by some athletes to gain competitive advantage. To monitor unauthorized androgen abuse, doping control programs use mass spectrometry (MS) to detect androgens, synthetic anabolic-androgenic steroids (AASs) and their metabolites in an athlete’s urine. AASs of unknown composition will not be detected by these procedures. Since AASs achieve their anabolic effects by activating the Androgen Receptor (AR), cell-based bioassays that measure the effect of a urine sample on AR activity are under investigation as complementary, pan-androgen detection methods. We evaluated an AR BioAssay as a monitor for androgen activity in urine pre-treated with glucuronidase, which releases T from the inactive T-glucuronide that predominates in urine. AR BioAssay activity levels were expressed as ‘T-equivalent’ concentrations by comparison to a T dose response curve. The T-equivalent concentrations of androgens in the urine of hypogonadal participants supplemented with T (in whom all androgenic activity should arise from T) were quantitatively identical to the T measurements conducted by MS at the UCLA Olympic Analytical Laboratory (0.96 ± 0.22). All 17 AASs studied were active in the AR BioAssay; other steroids were inactive. 12 metabolites of 10 commonly abused AASs, which are used for MS monitoring of AAS doping because of their prolonged presence in urine, had reduced or no AR BioAssay activity. Thus, the AR BioAssay can accurately and inexpensively monitor T, but its ability to monitor urinary AASs will be limited to a period immediately following doping in which the active AASs remain intact.

Characterization and Modulation of the Bitterness of Polymethoxyflavones Using Sensory and Receptor-Based Methods.

An obstacle in the application of many "health ingredients" is their alleged off-flavor. We used a combination of chemical, sensory, and biological analyses to identify the bitter components in citrus peel-derived polymethoxyflavone preparations, claimed to be functional in the lowering of cholesterol. Nobiletin (56-81%) and tangeretin (10-33%) were found to be the main bitter components. Using in vitro receptor assays, hTAS2R14 was shown to be the main bitter receptor involved in their perception, with EC50 values of 14 and 63 μM, respectively. Our analysis provided several routes for off-flavor reduction. Purification is an option because a purified, single PMF species proved to be considerably less bitter upon application in emulsified foods, due to limited solubility in the aqueous phase. A second route, also demonstrated in vivo, is C5-specific demethoxylation, in line with the finding that 5-desmethylnobiletin does not activate hTAS2R14. A third route could be the use of TAS2R14 antagonists. As a proof of principle, several antagonists, with IC50 values ranging from 10 to 50 μM, were identified.

ChemInform Abstract: Future De Novo Drug Design

AbstractReview: 63 refs.

Future De Novo Drug Design.

The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.

Analysis To Support Allergen Risk Management: Which Way To Go?

Food allergy represents an important food safety issue because of the potential lethal effects; the only effective treatment is the complete removal of the allergen involved from the diet. However, due to the growing complexity of food formulations and food processing, foods may be unintentionally contaminated via allergen-containing ingredients or cross-contamination. This affects not only consumers' well-being but also food producers and competent authorities involved in inspecting and auditing food companies. To address these issues, the food industry and control agencies rely on available analytical methods to quantify the amount of a particular allergic commodity in a food and thus to decide upon its safety. However, no "gold standard methods" exist for the quantitative detection of food allergens. Nowadays mostly receptor-based methods and in particular commercial kits are used in routine analysis. However, upon evaluation of their performances, commercial assays proved often to be unreliable in processed foods, attributed to the chemical changes in proteins that affect the molecular recognition with the receptor used. Unfortunately, the analytical outcome of other methods, among which are chromatographic combined with mass spectrometric techniques as well as DNA-based methods, seem to be affected in a comparable way by food processing. Several strategies can be employed to improve the quantitative analysis of allergens in foods. Nevertheless, issues related to extractability and matrix effects remain a permanent challenge. In view of the presented results, it is clear that the food industry needs to continue to make extra efforts to provide accurate labeling and to reduce the contamination with allergens to an acceptable level through the use of allergen risk management on a company level, which needs to be supported inevitably by a tailor-validated extraction and detection method.

QSAR Analysis of Human Adenosine A<sub>3</sub> Receptor Antagonists

Ligand-based and receptor-based methods were implemented together to investigate the binding modes of human adenosine A3 antagonists. First, pharmacophore models were developed using the HypoGen program with a training set of 18 diverse human adenosine A3 receptor antagonists from literature. Meanwhile, the three-dimensional structure of A3 receptor was modeled by homology modeling and molecular dynamics, and validated by PROCHECK. Molecular docking was conducted further to investigate receptor-ligand interactions. The pharmacophore model and homology models of A3 receptor matched well, allowing some important information to be obtained. One of the new pharmacophore models was used to screen the MDL drug database report (MDDR) including about 120000 compounds. As a result, eight candidate compounds that can be used for biological evaluation were discovered. These findings are important for the development and discovery of novel selective A3 antagonists and antiasthmatic compounds.

Molecular simulation of a series of benzothiazole PI3Kα inhibitors: probing the relationship between structural features, anti-tumor potency and selectivity

The phosphatidylinositol 3-kinase α (PI3Kα) was genetically validated as a promising therapeutic target for developing novel anticancer drugs. In order to explore the structure-activity correlation of benzothiazole series as inhibitors of PI3Kα, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) were performed on 61 promising molecules to build 3D-QSAR models based on both the ligand- and receptor-based methods. The best CoMFA and CoMSIA models had a cross-validated coefficient r(cv)(2) of 0.618 and 0.621, predicted correlation coefficient r(pred) (2) of 0.812 and 0.83, respectively, proving their high correlative and predictive abilities on both the training and test sets. In addition, docking analysis and molecular dynamics simulation (MD) were also applied to elucidate the probable binding modes of these inhibitors at the ATP binding pocket. Based on the contour maps and MD results, some key structural factors responsible for the activity of this series of compounds were revealed as follows: (1) Ring-A has a strong preference for bulky hydrophobic or aromatic groups; (2) Electron-withdrawing groups at the para position of ring-B and hydrophilic substituents in ring-B region may benefit the potency; (3) A polar substituent like -NHSO(2)- between ring-A and ring-B can enhance the activity of the drug by providing hydrogen bonding interaction with the protein target. The satisfactory results obtained from this work strongly suggest that the developed 3D-QSAR models and the obtained PI3Kα inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and be helpful in future PI3Kα inhibitor design.

Insights into the binding modes of human β3-adrenergic receptor agonists with ligand-based and receptor-based methods

Agonists of β(3)-adrenergic receptor (AR) have been thought as potential drugs for the treatment of obesity, type II diabetes, and overactive bladder. In order to clarify the essential structure-activity relationship and the detailed binding modes of β(3)-AR agonists as well as to identify new lead compounds activating β(3)-AR, ligand-based and receptor-based methods were applied. The pharmacophore models were developed based on 144 β(3)-AR agonists. Meanwhile, the homology model of the β(3)-AR was built based on the crystal structure of β(2)-AR. The pharmacophore model and the homology model mapped with each other very well, and some important information was obtained from the docking result. For example, agonists formed similar hydrogen-bonding interactions with residues Asp117, Arg315, and Asn332, π-π stacking interaction with residues Phe308, and hydrophobic interactions with residues Val118, Val121, Ala197, Phe198, Ala199, Phe309, and Phe328 of β(3)-AR. And the major difference about binding mode from the crystal structures of β(1)- and β(2)-ARs is the hydrogen-bonding interaction with the residue Arg315, which corresponds to the residue Asn313 of β(1)-AR and the residue His296 of β(2)-AR, respectively. Our findings may be crucial for the design and development of novel selective and potent β(3)-AR agonists.

Structural Determination of Three Different Series of Compounds as Hsp90 Inhibitors Using 3D-QSAR Modeling, Molecular Docking and Molecular Dynamics Methods

Hsp90 is involved in correcting, folding, maturation and activation of a diverse array of client proteins; it has also been implicated in the treatment of cancer in recent years. In this work, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), molecular docking and molecular dynamics were performed on three different series of Hsp90 inhibitors to build 3D-QSAR models, which were based on the ligand-based or receptor-based methods. The optimum 3D-QSAR models exhibited reasonable statistical characteristics with averaging internal q2 > 0.60 and external r2pred > 0.66 for Benzamide tetrahydro-4H-carbazol-4-one analogs (BT), AT13387 derivatives (AT) and Dihydroxylphenyl amides (DA). The results revealed that steric effects contributed the most to the BT model, whereas H-bonding was more important to AT, and electrostatic, hydrophobic, H-bond donor almost contributed equally to the DA model. The docking analysis showed that Asp93, Tyr139 and Thr184 in Hsp90 are important for the three series of inhibitors. Molecular dynamics simulation (MD) further indicated that the conformation derived from docking is basically consistent with the average structure extracted from MD simulation. These results not only lead to a better understanding of interactions between these inhibitors and Hsp90 receptor but also provide useful information for the design of new inhibitors with a specific activity.

Insights into binding modes of 5-HT2c receptor antagonists with ligand-based and receptor-based methods

5-hydroxytryptamine-2c (5-HT2c) receptor antagonists have clinical utility in the management of nervous system. In this work, ligand-based and receptor-based methods were used to investigate the binding mode of h5-HT2c receptor antagonists. First, the pharmacophore modeling of the h5-HT2c receptor antagonists was carried out by CATALYST. Then, the h5-HT2c antagonists were docked to the h5-HT2c receptor model. Subsequently, the comprehensive analysis of the pharmacophore and docking results revealed the structure-activity relationship of 5-HT2c receptor antagonists and the key residues involved in the interactions. For example, three hydrophobic points in the ligands corresponded to the region surrounded by Val135, Val208, Phe214, Ala222, Phe327, Phe328 and Val354 of the h5-HT2c receptor. The carbonyl group of compound 1 formed a hydrogen bond with Asn331. The nitrogen atom in the piperidine of compound 1 corresponding to the positive ionizable position of the best pharmacophore formed the electrostatic interactions with the carbonyl of Asp134, Asn331 and Val354, and with the hydroxyl group of Ser334. In addition, a predictive CoMFA model was developed based on the 24 compounds that were used as the training set in the pharmacophore modeling. Our results were not only useful to explore the detailed mechanism of the interactions between the h5-HT2c receptor and antagonists, but also provided suggestions in the discovery of novel 5-HT2c receptor antagonists.

Particulate Matter Source Apportionment: An Overview and Summary of Current Asian Studies

S-29C3-2 Background/Aims: An extensive literature review was conducted to evaluate the sources, composition, and ratios of particulate matter (PM) air pollution in Asian cities as part of a broader review of the literature on the health effects of air pollution in developing Asia. Methods: The reviewed air quality studies were published between 1995 and 2009 and were conducted in 16 Asian cities representing major urban conglomerates and background sites. Each reviewed study used receptor-based methods that include the following: Chemical Mass Balance Analysis, Principal Component Analysis, Positive Matrix Factorization, or other accepted chemical speciation methods. Results: The results obtained from these studies were regrouped into 6 different categories of sources that include: mobile, stationary, biomass, fugitive, secondary, and other. The highest fine PM concentrations were observed in Beijing, Hanoi, Delhi, and Kolkata reaching over 100-μg/m3 (annual average). There was great variation of source contribution by city suggesting the differences among the cities' PM problem. For some major cities, the ratios of PM2.5 to PM10 were calculated. South Asian cities seemed to have a higher coarse fraction in their PM compared to East Asian cities. In the South Asian cities of Dhaka, Rajshahi, Chennai, Islamabad, and Chak Shahzad, the ratios of PM2.5–PM10 were in the range of 0.22 and 0.35 illustrating that the coarse fraction dominated because of resuspended road dust, soil dust, and uncontrolled construction particles. With progressive development in recent years, this ratio is probably shifting to equal portion of both fine and coarse fractions as seen in many East Asian cities. In Bangkok, Beijing, Taiwan, and Bandung, the fine fraction slightly dominated the coarse fraction as vehicle emissions and industrial activity have progressively increased in the last decade. In more developed countries such as South Korea and Japan, this ratio was as high as 0.77. Conclusion: Together, the fine to coarse fraction PM ratio, the source contributions of PM, and the amount of fossil fuel burning and/or associated stage of development, provide a foundation for understanding the potential health risk and air quality status for some of these Asian cities. This work was funded by the Health Effects Institute.

Accuracy of receptor-based methods for detection of thyrotropin-receptor autoantibodies: a new automated third-generation immunoassay shows higher analytical and clinical sensitivity for the differential diagnosis of hyperthyroidism.

PurposeSpecific autoantibodies acting as TSH receptor agonists (TRAb) are responsible for Graves’ disease (GD). In the last 30 years three generations of assay methods for the detection of TRAb have become available. The aim of this multicentre study was to evaluate the analytical sensitivity, precision and diagnostic accuracy of TRAb measurement using a new automated assay in comparison with a second-generation standard method.MethodsSerum samples from patients with GD (n=82), autoimmune thyroiditis (AIT, n=57) or hyperthyroidism (HT, n=292), from 106 healthy subjects and from 57 patients with infectious diseases were analysed using a third-generation TRAb immunoassay (anti-TSHR, RAD 120; Radim, Italy) based on the human monoclonal TSH receptor antibody M22.ResultsUsing a cut-off value of 1.25 mIU/l, established by ROC curve analysis, 80/82 GD patients (97.5%), 68/292 HT patients (23.2%), and 6/57 AIT patients (10.5%) were TRAb-positive with the M22-based automated assay. The percentages of TRAb positivity were lower in the same patients when the measurements were done with the second-generation method (95.1%, 18.9%, 7.0%, respectively).ConclusionThe M22-based automated immunoassay shows high functional sensitivity (0.4 mIU/l) and high diagnostic specificity, is more sensitive than the standard second-generation method and is less time-consuming and labourintensive, and is therefore the up-to-date technology for TRAb detection in clinical practice.

Synthesis, 3D‐QSAR, and Structural Modeling of Benzolactam Derivatives with Binding Affinity for the D2 and D3 Receptors

A series of 37 benzolactam derivatives were synthesized, and their respective affinities for the dopamine D(2) and D(3) receptors evaluated. The relationships between structures and binding affinities were investigated using both ligand-based (3D-QSAR) and receptor-based methods. The results revealed the importance of diverse structural features in explaining the differences in the observed affinities, such as the location of the benzolactam carbonyl oxygen, or the overall length of the compounds. The optimal values for such ligand properties are slightly different for the D(2) and D(3) receptors, even though the binding sites present a very high degree of homology. We explain these differences by the presence of a hydrogen bond network in the D(2) receptor which is absent in the D(3) receptor and limits the dimensions of the binding pocket, causing residues in helix 7 to become less accessible. The implications of these results for the design of more potent and selective benzolactam derivatives are presented and discussed.

Insights into subtype selectivity of opioid agonists by ligand-based and structure-based methods

To probe the selective mechanism of agonists binding to three opioid receptor subtypes, ligand-based and receptor-based methods were implemented together and subtype characteristics of opioid agonists were clearly described. Three pharmacophore models of opioid agonists were generated by the Catalyst/HypoGen program. The best pharmacophore models for μ, δ and κ agonists contained four, five and five features, respectively. Meanwhile, the three-dimensional structures of three receptor subtypes were modeled on the basis of the crystal structure of β2-adrenergic receptor, and molecular docking was conducted further. According to these pharmacophore models and docking results, the similarities and differences among agonists of three subtypes were identified. μ or δ agonists, for example, could form one hydrogen bond separately with Tyr129 and Tyr150 at TMIII, whereas κ ones formed a π-π interaction in that place. These findings may be crucial for the development of novel selective analgesic drugs.

Insights into binding modes of adenosine A 2B antagonists with ligand-based and receptor-based methods

Ligand-based and receptor-based methods were used to investigate the binding modes of human adenosine A 2B antagonists. At first, pharmacophore models were developed based on 140 diverse A 2B antagonists from literature. Meanwhile, the structural model of A 2B receptor was built up based on the crystal structure of human A 2A receptor and validated by Induced Fit docking, Glide-XP and Glide-SP docking. Two models matched each other very well and some important implications were hence obtained. The residues of Phe173 and Glu174 in the second extracellular loop and Asn254 were crucial to the antagonists binding to form π–π stacking and hydrogen-bonding interactions. These findings would be very helpful for the discovery of novel and potent A 2B antagonists.

Fuzzy virtual ligands for virtual screening

A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single fuzzy VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers.

PocketGraph: graph representation of binding site volumes

The representation of small molecules as moleculargraphs [1] is a common technique in various fields ofcheminformatics. This approach employs abstractdescriptions of topology and properties for rapid analysesand comparison. Receptor-based methods in contrastmostly depend on more complex representations imped-ing simplified analysis and limiting the possibilities ofproperty assignment. In this study we demonstrate thatligand-based methods can be applied to receptor-derivedbinding site analysis.We introduce the new method PocketGraph that trans-lates representations of binding site volumes into lineargraphs and enables the application of graph-based meth-ods to the world of protein pockets. The method uses thePocketPicker [2] algorithm for characterization of bindingsite volumes and employs a Growing Neural Gas [3] pro-cedure to derive graph representations of pocket topolo-gies.Self-organizing map (SOM) projections revealed a limitednumber of pocket topologies. We argue that there is onlya small set of pocket shapes realized in the known ligand-receptor complexes.